Main model
# {.tabset}
res_mod_i <- metafor::rma.mv(logOR ~ Cut_off_date, V = seOR^2, random = ~ 1 | study_ID/es_id, data = dat_main)
res_mod_i
##
## Multivariate Meta-Analysis Model (k = 58; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0028 0.0530 54 no study_ID
## sigma^2.2 0.0000 0.0000 58 no study_ID/es_id
##
## Test for Residual Heterogeneity:
## QE(df = 56) = 82.4347, p-val = 0.0123
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9552, p-val = 0.1620
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0237 0.0162 1.4678 0.1422 -0.0080 0.0554
## Cut_off_dateProbable -0.0424 0.0303 -1.3983 0.1620 -0.1017 0.0170
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
res_mod <- metafor::rma.mv(logOR ~ Cut_off_date - 1, V = seOR^2, random = ~ 1 | study_ID/es_id, data = dat_main)
res_mod
##
## Multivariate Meta-Analysis Model (k = 58; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0028 0.0530 54 no study_ID
## sigma^2.2 0.0000 0.0000 58 no study_ID/es_id
##
## Test for Residual Heterogeneity:
## QE(df = 56) = 82.4347, p-val = 0.0123
##
## Test of Moderators (coefficients 1:2):
## QM(df = 2) = 2.6834, p-val = 0.2614
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## Cut_off_dateOfficial 0.0237 0.0162 1.4678 0.1422 -0.0080 0.0554
## Cut_off_dateProbable -0.0186 0.0256 -0.7274 0.4670 -0.0689 0.0316
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1